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All Journal Jurnal Pengabdian Masyarakat dan Riset Pendidikan Journal of Innovative and Creativity Jurnal Pengabdian Masyarakat Indonesia
Rahmat Irsyada
Politeknik Negeri Subang
Humanities Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Transformasi Digital UMKM: Pengembangan Marketplace BANGKIT (Belanja UMKM Kreatif, Inovatif, dan Komplit) untuk Ekspansi Penjualan Produk Lokal UMKM Kabupaten Subang Rahmat Irsyada; Lani Nurlani; Abd Rachman Mildan; Arnov Abdillah Rahman; Rachmad Augy; Nita Cahyani
JURNAL PENGABDIAN MASYARAKAT INDONESIA Vol. 4 No. 3 (2025): Oktober : Jurnal Pengabdian Masyarakat Indonesia (JPMI)
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jpmi.v4i3.6021

Abstract

Limitations in the adoption of digital technology are a major challenge for Micro, Small, and Medium Enterprises (MSMEs) in Subang Regency, which is reflected in the still-manual production process, conventional business management, and limited marketing reach on a local scale. On the other hand, the general public also faces low digital literacy which hinders participation in the modern economy. This community service program aims to address these problems through the design and development of an integrated marketplace platform called BANGKIT (Creative, Innovative, and Complete MSME Shopping). The program implementation method uses a participatory approach that includes three main stages: (1) development of the marketplace platform as a digital showcase for local products; (2) intensive training and mentoring for MSME actors regarding online store management, product photography, and digital marketing strategies; and (3) facilitation of the onboarding process for MSME products into the platform. The results of this activity are the realization of a functional digital economic ecosystem, increased capacity and empowerment of MSME partners, and expanded market reach for local products. This program not only provides concrete solutions for MSMEs, but also supports the achievement of the Key Performance Indicators (KPI) of higher education through the active involvement of lecturers and students in providing direct benefits to the community. Program outputs are disseminated through publications in community service journals, mass media, activity videos, poster works and reports on increasing the level of partner empowerment: management aspects.
Implementation of the K-Nearest Neighbor Method in a Web-Based Creditworthiness Decision Support System in Employee Cooperatives Nita Cahyani; Rahmat Irsyada; Hidayah Maulida
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

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Abstract

The Republic of Indonesia Employees' Cooperative (KPRI) was established as a legal entity based on the principles of family and people's economy, with a primary mandate to improve the welfare of its members. In its operations, savings and loan units are a crucial service. However, the cooperative's financial sustainability often faces serious challenges in the form of the risk of losses due to bad debts from debtors. This problem indicates that conventional methods for assessing prospective borrowers are often inaccurate and risk subjective, necessitating the need for stronger and more systematic criteria as a basis for decision-making. This research aims to address these issues by developing a Decision Support System (DSS) for loan eligibility. Through literature review and the collection of historical member transaction data, this research implements the K-Nearest Neighbor (K-NN) algorithm. This method was chosen for its ability to classify new loan eligibility based on similarity patterns (shortest distance) to previous customer data. The research results show that integrating the K-NN algorithm into the decision support system has a significant positive impact. The system has proven capable of providing classification recommendations that assist cooperative staff in processing loan applications according to predetermined criteria. System testing yielded a feasibility rate of 88%, indicating excellent performance. Overall, it can be concluded that the implementation of the K-NN method in KPRI loan approval processes makes the selection process more objective, accurate, and time-efficient compared to manual methods. This system is suitable for implementation as a strategic solution to minimize the risk of bad debt and maintain the financial stability of cooperatives.
Predicting Heart Failure Status Using Binary Logistic Regression with Clinical and Demographic Factors: Penelitian Nita Cahyani; Rahmat Irsyada
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.5189

Abstract

The aim of this study was to identify clinical and demographic characteristics associated with heart failure and develop an interpretable risk model using binary logistic regression on hospital patient data. Early detection of heart failure is expected to support timely intervention and clinical decision-making based on routine measurements. This study analyzed 130 anonymized patient data with heart failure status as a binary outcome. The initial logistic regression model included all candidate predictors and was then simplified to improve stability and calibration. Results are presented as odds ratios with 95% CIs. Performance evaluation included ROC–AUC, classification metrics, the Hosmer–Lemeshow test, calibration plots, and 5-fold cross-validation. The final model was significant (LR p = 1.0×10⁻⁵; McFadden R² = 0.222) with an accuracy of 81.54%, sensitivity of 89.41%, specificity of 66.67%, AUC of 0.811, and a Brier score of 0.164. Cross-validation showed an average AUC of 0.774 and an accuracy of 0.762. Significant predictors included BMI, serum creatinine, serum potassium, and total cholesterol, with acceptable calibration (p = 0.0767). This model has potential use as an interpretive screening tool, although external validation is still needed.
Application of the K-Nearest Neighbor Algorithm for Rainfall Prediction Based on Weather Conditions Nita Cahyani; Rahmat Irsyada
Journal of Innovative and Creativity (Joecy) Vol. 6 No. 2 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v6i2.13702

Abstract

ABSTRACT Weather and climate patterns in Indonesia are often erratic and difficult to predict. Sometimes, rain fails to fall during the rainy season, while conversely, heavy rains—sometimes leading to flooding—occur during the dry season. Such situations can cause hardship for the community; for instance, during the dry season, farmers may plant crops that require little water—such as tobacco—which are generally unsuitable for cultivation during the rainy season. Research based on BMKG data indicates that rainfall occurs during July and August, while the dry season begins in May and June. To address these issues, scientific prediction can be employed by analyzing actual or historical data; this involves a process of analyzing current data to forecast future events, utilizing data mining for prediction and the K-Nearest Neighbor (K-NN) algorithm for classification. The research model in this thesis employs a development methodology covering software design, modeling, construction, and system delivery to the end-user. The system development follows the Waterfall model. The Weather Prediction System utilizes the K-Nearest Neighbor algorithm—implemented via PHP, MySQL, and RapidMiner—to facilitate future rainfall prediction. Data mining calculations regarding factors influencing rainfall using the K-NN method (with a 70:30 training-to-testing data ratio) yielded the following accuracy results: 98.87% for K=3, 97.30% for K=5, and 97.16% for K=7. Consequently, the use of the K-Nearest Neighbor method for prediction can assist in resolving these issues.
Co-Authors Abd Rachman Mildan Arnov Abdillah Rahman Hidayah Maulida Lani Nurlani Nita Cahyani Rachmad Augy